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The trend towards increasingly deep neural networks has been driven by a general observation that increasing depth increases the performance of a network.
Learning long-term dependencies with gradient descent is difficult
Y. Bengio, P. Simard, and P. Frasconi · 1994
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Semantic contours from inverse detectors
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik · 2011
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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The PASCAL visual object classes challenge: A retrospective
M. Everingham, S. Eslami, L. van Gool, C. Williams, J. Winn, and A. Zisserman · 2014
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Microsoft COCO: Common objects in context
T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. R. P. Dollár, and C. Zitnick · 2014
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The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N. Cho, S. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Semantic image segmentation with deep convolutional nets and fully connected CRFs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille · 2015
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MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang · 2015
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BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J. Dai, K. He, and J. Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Semantic image segmentation via deep parsing network
Z. Liu, X. Li, P. Luo, C. Loy, and X. Tang · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2015
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Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr · 2015
Fractalnet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
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Exploring context with deep structured models for semantic segmentation
G. Lin, C. Shen, A. van den Hengel, and I. Reid · 2016
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The loss surface of residual networks: Ensembles and the role of batch normalization
E. Littwin and L. Wolf · 2016
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Systematic evaluation of CNN advances on the ImageNet
D. Mishkin, N. Sergievskiy, and J. Matas · 2016
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Inception-v4, Inception-Resnet and the impact of residual connections on learning
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
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The Cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Instance-aware semantic segmentation via multi-task network cascades
J. Dai, K. He, and J. Sun · 2016
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Laplacian pyramid reconstruction and refinement for semantic segmentation
G. Ghiasi and C. C. Fowlkes · 2016
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Training and investigating residual nets
S. Gross and M. Wilber · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi · 2016
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Residual networks behave like ensembles of relatively shallow networks
A. Veit, M. Wilber, and S. Belongie · 2016
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J. Wang, Z. Wei, T. Zhang, and W. Zeng · 2016
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Dense CNN learning with equivalent mappings
J. Wu, C.-W. Xie, and J.-H. Luo · 2016
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Bridging category-level and instance-level semantic image segmentation
Z. Wu, C. Shen, and A. van den Hengel · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Understanding scene in the wild
H. Zhao, J. Shi, X. Qi, X. Wang, T. Xiao, and J. Jia · 2016
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Places: An image database for deep scene understanding
B. Zhou, A. Khosla, A. Lapedriza, A. Torralba, and A. Oliva · 2016
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Semantic understanding of scenes through ADE20K dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2016
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